1.LOAD THE DATA

CONVERT FLOAT TO INTEGERS

COUNT

2.Exploratory data analysis

CHECK FOR MISSING DATA

BINNING THE DATA

CATEGORICAL DATA

CONTINOUS DATA

DATA VISULIZATION

HANDLING OUTLIERS

DETECTING OUTLIERS

REMOVE OUTLIERS

SEPARATING FEATURES FROM THE LABEL

FEATURE SCALING

SPLITTING THE DATA

3. MODEL BUILDING

Logistic Regression

Decision Tree

Random Forest Classifier

K-Nearest Neighbors

RandomForestClassifier model,is the best of the 4,as it has the highest Accuracy percentage score

4. MODEL PERFOMANCE ANALYSIS

CLASSIFICATION REPORT AND CONFUSION MATRIX

ROC_CURVE

FEATURE IMPORTANCE

PANDA PROFILING REPORT